1. Field of the Invention
The present invention relates to learning system and method, recognition device and method, creation device and method, recognition and creation device and method, and programs, and more particularly, to learning system and method, recognition device and method, creation device and method, recognition and creation device and method, and programs, capable of autonomically learning a plurality of dynamics from measured time-series data and recognizing input time-series data are recognized or creating and outputting time-series data from a predetermined input on the basis of the learning result.
2. Description of the Related Art
It was known that actions (movements) of a system such as a robot can be described as dynamical systems determined by a rule of time evolution and the dynamical systems of various actions can be embodied by a specific attractor dynamics.
For example, a walking movement of a bipedal robot like a human being can be described in limit cycle dynamics in which a movement state of a system is secured to a certain specific cyclic orbit from a variety of initial states (for example, see G. Taga, 1998, “Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment”, Biological Cybernetics, 65, 147-159 (Non-patent Document 1) and Gentaro Taga, “Nonlinear Dynamical System and Development of Dynamical Design Movement and Recognition of Brain and Body”, published by Kanekoshobo (Non-patent Document 2). A reaching movement of an arm robot like extending a hand to an object can be described in fixed-point dynamics in which the movement is secured to a certain fixed-point from a variety of initial states. It could be also said that all the movements can be embodied by a combination of a discrete movement describable in the fixed-point dynamics and a cyclic movement describable in the limit cycle dynamics.
When controlling actions (movements) of a system such as a robot by the use of the attractor dynamics, it is important, first, how to construct the attractor dynamics depending on the tasks and second, that a corresponding motor power is created in accordance with the attractor dynamics on the basis of information acquired from an input to a sensor. In order to solve the important points, it is necessary to create action outputs (movement outputs) of the system so that the attractor dynamics continuously interact with environments.
There has been suggested a method of learning such attract dynamics instead of a person's design. For example, a method using a recurrent neural network (hereinafter, referred to as RNN) is known. As the RNN, a network which has a context unit coupled to the network in a feedback loop and which can theoretically approximate a dynamical system by holding an inner state therein is known. However, in a learning model including a network module coupled densely, when a plurality of dynamics necessary for learning a large scale of actions is learned, the interference between dynamics to be stored is too large, thereby making the learning difficult. Accordingly, there have been suggested several learning models employing a modular architecture in which a set of a plurality of network modules constitutes a learning model. In the modular architecture, the number of dynamics to be stored can be easily increased by increasing the number of modules in principle, but there is a problem with a module selection determining with which module to learn a given learning sample.
The method of performing the module selection is classified into two methods of a supervised learning method in which a person determines to which module the learning sample (learning data) should be assigned and an unsupervised learning method in which a learning model autonomically determines the module. In order for a robot or a system to autonomically perform a learning operation, it is necessary to perform a learning operation of a module by the use of the unsupervised learning method.
As an example of the module learning method, there has been suggested a learning model called a mixture of RNN expert (for example, see JP-A-11-126198 (Patent Document 1)). In the learning model, outputs of a plurality of RNN modules are integrated in a gate mechanism to determine the final output and the learning of the RNN modules is performed while adjusting the gate mechanism by the use of a maximum likelihood estimation method. However, in the method based on such entire optimization, there is a problem in that it is difficult to perform the learning operation when the number of modules is great. On the contrary, in methods using a self-organization map (hereinafter, referred to as SOM) (for example, see T. Kohonen, “Self-organization Map”, published by Springer Verlag, Tokyo (Non-patent Document 3)) used to learn a category of a vector pattern or neural-gas (for example, see T. M. Martinetz, S. G. Berkovich, K. J. Schulten, ““Neural-Gas” Network for Vector Quantization and its Application to Time-Series Prediction”, IEEE Trans. Neural Networks, VOL. 4, No. 4, pp. 558-569, 1999 (Non-patent Document 4)), it is known that a learning rule based on the entire optimization is not used and thus the optimization is not ensured, but an appropriate category architecture can be self-organizationally learned in an unsupervised learning manner. In the methods, it is possible to practically perform the learning even when the number of modules is great.